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There are various scientific and technological areas in which it is imperative to rapidly detect and quantify changes in
imagery over time. In fields such as earth remote sensing, aerospace systems, and medical imaging, searching for timedependent,
regional changes across deformable topographies is complicated by varying camera acquisition geometries,
lighting environments, background clutter conditions, and occlusion. Under these constantly-fluctuating conditions, the
use of standard, rigid-body registration approaches often fail to provide sufficient fidelity to overlay image scenes
together. This is problematic because incorrect assessments of the underlying changes of high-level topography can
result in systematic errors in the quantification and classification of interested areas.
For example, in the current naked-eye detection strategies of melanoma, a dermatologist often uses static morphological
attributes to identify suspicious skin lesions for biopsy. This approach does not incorporate temporal changes which
suggest malignant degeneration. By performing the co-registration of time-separated skin imagery, a dermatologist may
more effectively detect and identify early morphological changes in pigmented lesions; enabling the physician to detect
cancers at an earlier stage resulting in decreased morbidity and mortality.
This paper describes an image processing system which will be used to detect changes in the characteristics of skin
lesions over time. The proposed system consists of three main functional elements: 1.) coarse alignment of timesequenced
imagery, 2.) refined alignment of local skin topographies, and 3.) assessment of local changes in lesion size.
During the coarse alignment process, various approaches can be used to obtain a rough alignment, including: 1.) a
manual landmark/intensity-based registration method1, and 2.) several flavors of autonomous optical matched filter
methods2. These procedures result in the rough alignment of a patient's back topography. Since the skin is a deformable
membrane, this process only provides an initial condition for subsequent refinements in aligning the localized
topography of the skin. To achieve a refined enhancement, a Particle Swarm Optimizer (PSO) is used to optimally
determine the local camera models associated with a generalized geometric transform. Here the optimization process is
driven using the minimization of entropy between the multiple time-separated images. Once the camera models are
corrected for local skin deformations, the images are compared using both pixel-based and regional-based methods.
Limits on the detectability of change are established by the fidelity to which the algorithm corrects for local skin
deformation and background alterations. These limits provide essential information in establishing early-warning
thresholds for Melanoma detection.
Key to this work is the development of a PSO alignment algorithm to perform the refined alignment in local skin
topography between the time sequenced imagery (TSI). Test and validation of this alignment process is achieved using a
forward model producing known geometric artifacts in the images and afterwards using a PSO algorithm to demonstrate
the ability to identify and correct for these artifacts. Specifically, the forward model introduces local translational,
rotational, and magnification changes within the image. These geometric modifiers are expected during TSI acquisition
because of logistical issues to precisely align the patient to the image recording geometry and is therefore of paramount importance to any viable image registration system. This paper shows that the PSO alignment algorithm is effective in
autonomously determining and mitigating these geometric modifiers. The degree of efficacy is measured by several
statistically and morphologically based pre-image filtering operations applied to the TSI imagery before applying the
PSO alignment algorithm. These trade studies show that global image threshold binarization provides rapid and superior
convergence characteristics relative to that of morphologically based methods.
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